Abstract
Since 1990s, with an advancement of network technology and the popularization of the Internet, information that people can access has proliferated, thus information recommendation has been investigated as an important issue. Because preference to information recommendation can be different as context that the users are related to, we should consider this context to provide a good service. This paper proposes the recommendation system that considers the preferences of group users in mobile environment and applied the system to recommendation of restaurants. Since mobile environment has plenty of uncertainty, our system have used Bayesian network which showed reliable performance with uncertain input to model individual user’s preference. Also, restaurant recommendation mostly considers the preference of group users, so we have used AHP (Analytic Hierarchy Process) of multi-criteria decision making method to get the preference of group users from individual users’ preferences. For experiments, we have assumed 10 different situations and compared the proposed method with random recommendation and simple rule-based recommendation. Finally, we have confirmed that the proposed system provides high usability with SUS (System Usability Scale).
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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE T Knowl Data EN 17(6), 734–749 (2005)
Lieberman, H., et al.: Let’s browse: A collaborative web browsing agent. In: Proc. of the Int. Conf. on Intelligent User Interfaces, pp. 65–68 (1998)
O’Connor, M., et al.: PolyLens: A recommender system for groups of users. In: Proc. of the European Conf. on Computer-Supported Cooperative Work, pp. 199–218 (2000)
Dey, A.K.: Understanding and using context. Personal and Ubiquitous Computing 5, 20–24 (2001)
Tewari, G., et al.: Personalized location-based brokering using an agent-based intermediary architecture. Decis Support Syst. 34(2), 127–137 (2003)
Kim, C.Y., et al.: Viscors: A visual-content recommender for the mobile web. IEEE Intell Syst. 19(6), 32–39 (2004)
Cooper, G., Herskovits, E.A.: A Bayesian method for the induction of probabilistic networks from data. Lach Learn 9(4), 109–347 (1992)
Korpipaa, P., et al.: Bayesian approach to sensor-based context awareness. Personal and Ubiquitous Computing 7(2), 113–124 (2003)
Horvitz, E., et al.: Models of attention in computing and communications: From principles to applications. Commun. Acm 46(3), 52–59 (2003)
Saaty, T.L.: Multicriteria Decision Making: The Analytic Hierarchy Process, Planning, Priority Setting, Resource Allocation. RWS Publications (1990)
Brooke, J.: SUS: A “quick and dirty” Usability Scale. In: Jordan, P.W., Thomas, B., Weerdmeester, B.A., McClelland, A.L. (eds.) Usability Evaluation in INdustry. Taylor and Francis, London (1996)
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Park, MH., Park, HS., Cho, SB. (2008). Restaurant Recommendation for Group of People in Mobile Environments Using Probabilistic Multi-criteria Decision Making. In: Lee, S., Choo, H., Ha, S., Shin, I.C. (eds) Computer-Human Interaction. APCHI 2008. Lecture Notes in Computer Science, vol 5068. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70585-7_13
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DOI: https://doi.org/10.1007/978-3-540-70585-7_13
Publisher Name: Springer, Berlin, Heidelberg
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